From 3b8c277236ef50787be9281d0f176fe5e6b486c2 Mon Sep 17 00:00:00 2001
From: zain-cs <mzainulabidin.cs@gmail.com>
Date: Tue, 23 Jun 2026 16:11:07 +0500
Subject: [PATCH 1/3] add credit card fraud detection using DBSCAN clustering

---
 .../DBSCAN_clustering.ipynb                   | 1355 +++++++++++++++++
 credit-card-fraud-detection/README.md         |   31 +
 credit-card-fraud-detection/requirements.txt  |    4 +
 3 files changed, 1390 insertions(+)
 create mode 100644 credit-card-fraud-detection/DBSCAN_clustering.ipynb
 create mode 100644 credit-card-fraud-detection/README.md
 create mode 100644 credit-card-fraud-detection/requirements.txt

diff --git a/credit-card-fraud-detection/DBSCAN_clustering.ipynb b/credit-card-fraud-detection/DBSCAN_clustering.ipynb
new file mode 100644
index 0000000..54f0773
--- /dev/null
+++ b/credit-card-fraud-detection/DBSCAN_clustering.ipynb
@@ -0,0 +1,1355 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "6vPIHEvB5ggH",
+        "outputId": "8de9878e-fdd1-4bf1-fdd3-d783dd4d3ed9"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-3a06c761-ca18-4e78-9fb2-70845db888aa\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-3a06c761-ca18-4e78-9fb2-70845db888aa\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving Breast_cancer_dataset (1).csv to Breast_cancer_dataset (1) (1).csv\n",
+            "✅ File Loaded Successfully!\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
+              "0    842302         M        17.99         10.38          122.80     1001.0   \n",
+              "1    842517         M        20.57         17.77          132.90     1326.0   \n",
+              "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
+              "3  84348301         M        11.42         20.38           77.58      386.1   \n",
+              "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
+              "\n",
+              "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
+              "0          0.11840           0.27760          0.3001              0.14710   \n",
+              "1          0.08474           0.07864          0.0869              0.07017   \n",
+              "2          0.10960           0.15990          0.1974              0.12790   \n",
+              "3          0.14250           0.28390          0.2414              0.10520   \n",
+              "4          0.10030           0.13280          0.1980              0.10430   \n",
+              "\n",
+              "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
+              "0  ...          17.33           184.60      2019.0            0.1622   \n",
+              "1  ...          23.41           158.80      1956.0            0.1238   \n",
+              "2  ...          25.53           152.50      1709.0            0.1444   \n",
+              "3  ...          26.50            98.87       567.7            0.2098   \n",
+              "4  ...          16.67           152.20      1575.0            0.1374   \n",
+              "\n",
+              "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
+              "0             0.6656           0.7119                0.2654          0.4601   \n",
+              "1             0.1866           0.2416                0.1860          0.2750   \n",
+              "2             0.4245           0.4504                0.2430          0.3613   \n",
+              "3             0.8663           0.6869                0.2575          0.6638   \n",
+              "4             0.2050           0.4000                0.1625          0.2364   \n",
+              "\n",
+              "   fractal_dimension_worst  Unnamed: 32  \n",
+              "0                  0.11890          NaN  \n",
+              "1                  0.08902          NaN  \n",
+              "2                  0.08758          NaN  \n",
+              "3                  0.17300          NaN  \n",
+              "4                  0.07678          NaN  \n",
+              "\n",
+              "[5 rows x 33 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-e755260c-f509-4714-8c79-961c52e72b18\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>id</th>\n",
+              "      <th>diagnosis</th>\n",
+              "      <th>radius_mean</th>\n",
+              "      <th>texture_mean</th>\n",
+              "      <th>perimeter_mean</th>\n",
+              "      <th>area_mean</th>\n",
+              "      <th>smoothness_mean</th>\n",
+              "      <th>compactness_mean</th>\n",
+              "      <th>concavity_mean</th>\n",
+              "      <th>concave points_mean</th>\n",
+              "      <th>...</th>\n",
+              "      <th>texture_worst</th>\n",
+              "      <th>perimeter_worst</th>\n",
+              "      <th>area_worst</th>\n",
+              "      <th>smoothness_worst</th>\n",
+              "      <th>compactness_worst</th>\n",
+              "      <th>concavity_worst</th>\n",
+              "      <th>concave points_worst</th>\n",
+              "      <th>symmetry_worst</th>\n",
+              "      <th>fractal_dimension_worst</th>\n",
+              "      <th>Unnamed: 32</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>842302</td>\n",
+              "      <td>M</td>\n",
+              "      <td>17.99</td>\n",
+              "      <td>10.38</td>\n",
+              "      <td>122.80</td>\n",
+              "      <td>1001.0</td>\n",
+              "      <td>0.11840</td>\n",
+              "      <td>0.27760</td>\n",
+              "      <td>0.3001</td>\n",
+              "      <td>0.14710</td>\n",
+              "      <td>...</td>\n",
+              "      <td>17.33</td>\n",
+              "      <td>184.60</td>\n",
+              "      <td>2019.0</td>\n",
+              "      <td>0.1622</td>\n",
+              "      <td>0.6656</td>\n",
+              "      <td>0.7119</td>\n",
+              "      <td>0.2654</td>\n",
+              "      <td>0.4601</td>\n",
+              "      <td>0.11890</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>842517</td>\n",
+              "      <td>M</td>\n",
+              "      <td>20.57</td>\n",
+              "      <td>17.77</td>\n",
+              "      <td>132.90</td>\n",
+              "      <td>1326.0</td>\n",
+              "      <td>0.08474</td>\n",
+              "      <td>0.07864</td>\n",
+              "      <td>0.0869</td>\n",
+              "      <td>0.07017</td>\n",
+              "      <td>...</td>\n",
+              "      <td>23.41</td>\n",
+              "      <td>158.80</td>\n",
+              "      <td>1956.0</td>\n",
+              "      <td>0.1238</td>\n",
+              "      <td>0.1866</td>\n",
+              "      <td>0.2416</td>\n",
+              "      <td>0.1860</td>\n",
+              "      <td>0.2750</td>\n",
+              "      <td>0.08902</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>84300903</td>\n",
+              "      <td>M</td>\n",
+              "      <td>19.69</td>\n",
+              "      <td>21.25</td>\n",
+              "      <td>130.00</td>\n",
+              "      <td>1203.0</td>\n",
+              "      <td>0.10960</td>\n",
+              "      <td>0.15990</td>\n",
+              "      <td>0.1974</td>\n",
+              "      <td>0.12790</td>\n",
+              "      <td>...</td>\n",
+              "      <td>25.53</td>\n",
+              "      <td>152.50</td>\n",
+              "      <td>1709.0</td>\n",
+              "      <td>0.1444</td>\n",
+              "      <td>0.4245</td>\n",
+              "      <td>0.4504</td>\n",
+              "      <td>0.2430</td>\n",
+              "      <td>0.3613</td>\n",
+              "      <td>0.08758</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>84348301</td>\n",
+              "      <td>M</td>\n",
+              "      <td>11.42</td>\n",
+              "      <td>20.38</td>\n",
+              "      <td>77.58</td>\n",
+              "      <td>386.1</td>\n",
+              "      <td>0.14250</td>\n",
+              "      <td>0.28390</td>\n",
+              "      <td>0.2414</td>\n",
+              "      <td>0.10520</td>\n",
+              "      <td>...</td>\n",
+              "      <td>26.50</td>\n",
+              "      <td>98.87</td>\n",
+              "      <td>567.7</td>\n",
+              "      <td>0.2098</td>\n",
+              "      <td>0.8663</td>\n",
+              "      <td>0.6869</td>\n",
+              "      <td>0.2575</td>\n",
+              "      <td>0.6638</td>\n",
+              "      <td>0.17300</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>84358402</td>\n",
+              "      <td>M</td>\n",
+              "      <td>20.29</td>\n",
+              "      <td>14.34</td>\n",
+              "      <td>135.10</td>\n",
+              "      <td>1297.0</td>\n",
+              "      <td>0.10030</td>\n",
+              "      <td>0.13280</td>\n",
+              "      <td>0.1980</td>\n",
+              "      <td>0.10430</td>\n",
+              "      <td>...</td>\n",
+              "      <td>16.67</td>\n",
+              "      <td>152.20</td>\n",
+              "      <td>1575.0</td>\n",
+              "      <td>0.1374</td>\n",
+              "      <td>0.2050</td>\n",
+              "      <td>0.4000</td>\n",
+              "      <td>0.1625</td>\n",
+              "      <td>0.2364</td>\n",
+              "      <td>0.07678</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 33 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e755260c-f509-4714-8c79-961c52e72b18')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-e755260c-f509-4714-8c79-961c52e72b18 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-e755260c-f509-4714-8c79-961c52e72b18');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-6b324c6e-8f4c-4c72-94db-78ea1626a4ff\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6b324c6e-8f4c-4c72-94db-78ea1626a4ff')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-6b324c6e-8f4c-4c72-94db-78ea1626a4ff button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "📋 Columns in dataset:\n",
+            "['id', 'diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean', 'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean', 'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se', 'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se', 'fractal_dimension_se', 'radius_worst', 'texture_worst', 'perimeter_worst', 'area_worst', 'smoothness_worst', 'compactness_worst', 'concavity_worst', 'concave points_worst', 'symmetry_worst', 'fractal_dimension_worst', 'Unnamed: 32']\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "   diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
+              "0          1        17.99         10.38          122.80     1001.0   \n",
+              "1          1        20.57         17.77          132.90     1326.0   \n",
+              "2          1        19.69         21.25          130.00     1203.0   \n",
+              "3          1        11.42         20.38           77.58      386.1   \n",
+              "4          1        20.29         14.34          135.10     1297.0   \n",
+              "5          1        12.45         15.70           82.57      477.1   \n",
+              "6          1        18.25         19.98          119.60     1040.0   \n",
+              "7          1        13.71         20.83           90.20      577.9   \n",
+              "8          1        13.00         21.82           87.50      519.8   \n",
+              "9          1        12.46         24.04           83.97      475.9   \n",
+              "\n",
+              "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
+              "0          0.11840           0.27760         0.30010              0.14710   \n",
+              "1          0.08474           0.07864         0.08690              0.07017   \n",
+              "2          0.10960           0.15990         0.19740              0.12790   \n",
+              "3          0.14250           0.28390         0.24140              0.10520   \n",
+              "4          0.10030           0.13280         0.19800              0.10430   \n",
+              "5          0.12780           0.17000         0.15780              0.08089   \n",
+              "6          0.09463           0.10900         0.11270              0.07400   \n",
+              "7          0.11890           0.16450         0.09366              0.05985   \n",
+              "8          0.12730           0.19320         0.18590              0.09353   \n",
+              "9          0.11860           0.23960         0.22730              0.08543   \n",
+              "\n",
+              "   symmetry_mean  ...  texture_worst  perimeter_worst  area_worst  \\\n",
+              "0         0.2419  ...          17.33           184.60      2019.0   \n",
+              "1         0.1812  ...          23.41           158.80      1956.0   \n",
+              "2         0.2069  ...          25.53           152.50      1709.0   \n",
+              "3         0.2597  ...          26.50            98.87       567.7   \n",
+              "4         0.1809  ...          16.67           152.20      1575.0   \n",
+              "5         0.2087  ...          23.75           103.40       741.6   \n",
+              "6         0.1794  ...          27.66           153.20      1606.0   \n",
+              "7         0.2196  ...          28.14           110.60       897.0   \n",
+              "8         0.2350  ...          30.73           106.20       739.3   \n",
+              "9         0.2030  ...          40.68            97.65       711.4   \n",
+              "\n",
+              "   smoothness_worst  compactness_worst  concavity_worst  concave points_worst  \\\n",
+              "0            0.1622             0.6656           0.7119                0.2654   \n",
+              "1            0.1238             0.1866           0.2416                0.1860   \n",
+              "2            0.1444             0.4245           0.4504                0.2430   \n",
+              "3            0.2098             0.8663           0.6869                0.2575   \n",
+              "4            0.1374             0.2050           0.4000                0.1625   \n",
+              "5            0.1791             0.5249           0.5355                0.1741   \n",
+              "6            0.1442             0.2576           0.3784                0.1932   \n",
+              "7            0.1654             0.3682           0.2678                0.1556   \n",
+              "8            0.1703             0.5401           0.5390                0.2060   \n",
+              "9            0.1853             1.0580           1.1050                0.2210   \n",
+              "\n",
+              "   symmetry_worst  fractal_dimension_worst  Cluster  \n",
+              "0          0.4601                  0.11890       -1  \n",
+              "1          0.2750                  0.08902        0  \n",
+              "2          0.3613                  0.08758       -1  \n",
+              "3          0.6638                  0.17300       -1  \n",
+              "4          0.2364                  0.07678       -1  \n",
+              "5          0.3985                  0.12440       -1  \n",
+              "6          0.3063                  0.08368        0  \n",
+              "7          0.3196                  0.11510       -1  \n",
+              "8          0.4378                  0.10720       -1  \n",
+              "9          0.4366                  0.20750       -1  \n",
+              "\n",
+              "[10 rows x 32 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-cdf2a5af-29b0-43f6-baef-5196d49f08c3\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>diagnosis</th>\n",
+              "      <th>radius_mean</th>\n",
+              "      <th>texture_mean</th>\n",
+              "      <th>perimeter_mean</th>\n",
+              "      <th>area_mean</th>\n",
+              "      <th>smoothness_mean</th>\n",
+              "      <th>compactness_mean</th>\n",
+              "      <th>concavity_mean</th>\n",
+              "      <th>concave points_mean</th>\n",
+              "      <th>symmetry_mean</th>\n",
+              "      <th>...</th>\n",
+              "      <th>texture_worst</th>\n",
+              "      <th>perimeter_worst</th>\n",
+              "      <th>area_worst</th>\n",
+              "      <th>smoothness_worst</th>\n",
+              "      <th>compactness_worst</th>\n",
+              "      <th>concavity_worst</th>\n",
+              "      <th>concave points_worst</th>\n",
+              "      <th>symmetry_worst</th>\n",
+              "      <th>fractal_dimension_worst</th>\n",
+              "      <th>Cluster</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>17.99</td>\n",
+              "      <td>10.38</td>\n",
+              "      <td>122.80</td>\n",
+              "      <td>1001.0</td>\n",
+              "      <td>0.11840</td>\n",
+              "      <td>0.27760</td>\n",
+              "      <td>0.30010</td>\n",
+              "      <td>0.14710</td>\n",
+              "      <td>0.2419</td>\n",
+              "      <td>...</td>\n",
+              "      <td>17.33</td>\n",
+              "      <td>184.60</td>\n",
+              "      <td>2019.0</td>\n",
+              "      <td>0.1622</td>\n",
+              "      <td>0.6656</td>\n",
+              "      <td>0.7119</td>\n",
+              "      <td>0.2654</td>\n",
+              "      <td>0.4601</td>\n",
+              "      <td>0.11890</td>\n",
+              "      <td>-1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>20.57</td>\n",
+              "      <td>17.77</td>\n",
+              "      <td>132.90</td>\n",
+              "      <td>1326.0</td>\n",
+              "      <td>0.08474</td>\n",
+              "      <td>0.07864</td>\n",
+              "      <td>0.08690</td>\n",
+              "      <td>0.07017</td>\n",
+              "      <td>0.1812</td>\n",
+              "      <td>...</td>\n",
+              "      <td>23.41</td>\n",
+              "      <td>158.80</td>\n",
+              "      <td>1956.0</td>\n",
+              "      <td>0.1238</td>\n",
+              "      <td>0.1866</td>\n",
+              "      <td>0.2416</td>\n",
+              "      <td>0.1860</td>\n",
+              "      <td>0.2750</td>\n",
+              "      <td>0.08902</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1</td>\n",
+              "      <td>19.69</td>\n",
+              "      <td>21.25</td>\n",
+              "      <td>130.00</td>\n",
+              "      <td>1203.0</td>\n",
+              "      <td>0.10960</td>\n",
+              "      <td>0.15990</td>\n",
+              "      <td>0.19740</td>\n",
+              "      <td>0.12790</td>\n",
+              "      <td>0.2069</td>\n",
+              "      <td>...</td>\n",
+              "      <td>25.53</td>\n",
+              "      <td>152.50</td>\n",
+              "      <td>1709.0</td>\n",
+              "      <td>0.1444</td>\n",
+              "      <td>0.4245</td>\n",
+              "      <td>0.4504</td>\n",
+              "      <td>0.2430</td>\n",
+              "      <td>0.3613</td>\n",
+              "      <td>0.08758</td>\n",
+              "      <td>-1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1</td>\n",
+              "      <td>11.42</td>\n",
+              "      <td>20.38</td>\n",
+              "      <td>77.58</td>\n",
+              "      <td>386.1</td>\n",
+              "      <td>0.14250</td>\n",
+              "      <td>0.28390</td>\n",
+              "      <td>0.24140</td>\n",
+              "      <td>0.10520</td>\n",
+              "      <td>0.2597</td>\n",
+              "      <td>...</td>\n",
+              "      <td>26.50</td>\n",
+              "      <td>98.87</td>\n",
+              "      <td>567.7</td>\n",
+              "      <td>0.2098</td>\n",
+              "      <td>0.8663</td>\n",
+              "      <td>0.6869</td>\n",
+              "      <td>0.2575</td>\n",
+              "      <td>0.6638</td>\n",
+              "      <td>0.17300</td>\n",
+              "      <td>-1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1</td>\n",
+              "      <td>20.29</td>\n",
+              "      <td>14.34</td>\n",
+              "      <td>135.10</td>\n",
+              "      <td>1297.0</td>\n",
+              "      <td>0.10030</td>\n",
+              "      <td>0.13280</td>\n",
+              "      <td>0.19800</td>\n",
+              "      <td>0.10430</td>\n",
+              "      <td>0.1809</td>\n",
+              "      <td>...</td>\n",
+              "      <td>16.67</td>\n",
+              "      <td>152.20</td>\n",
+              "      <td>1575.0</td>\n",
+              "      <td>0.1374</td>\n",
+              "      <td>0.2050</td>\n",
+              "      <td>0.4000</td>\n",
+              "      <td>0.1625</td>\n",
+              "      <td>0.2364</td>\n",
+              "      <td>0.07678</td>\n",
+              "      <td>-1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>1</td>\n",
+              "      <td>12.45</td>\n",
+              "      <td>15.70</td>\n",
+              "      <td>82.57</td>\n",
+              "      <td>477.1</td>\n",
+              "      <td>0.12780</td>\n",
+              "      <td>0.17000</td>\n",
+              "      <td>0.15780</td>\n",
+              "      <td>0.08089</td>\n",
+              "      <td>0.2087</td>\n",
+              "      <td>...</td>\n",
+              "      <td>23.75</td>\n",
+              "      <td>103.40</td>\n",
+              "      <td>741.6</td>\n",
+              "      <td>0.1791</td>\n",
+              "      <td>0.5249</td>\n",
+              "      <td>0.5355</td>\n",
+              "      <td>0.1741</td>\n",
+              "      <td>0.3985</td>\n",
+              "      <td>0.12440</td>\n",
+              "      <td>-1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>1</td>\n",
+              "      <td>18.25</td>\n",
+              "      <td>19.98</td>\n",
+              "      <td>119.60</td>\n",
+              "      <td>1040.0</td>\n",
+              "      <td>0.09463</td>\n",
+              "      <td>0.10900</td>\n",
+              "      <td>0.11270</td>\n",
+              "      <td>0.07400</td>\n",
+              "      <td>0.1794</td>\n",
+              "      <td>...</td>\n",
+              "      <td>27.66</td>\n",
+              "      <td>153.20</td>\n",
+              "      <td>1606.0</td>\n",
+              "      <td>0.1442</td>\n",
+              "      <td>0.2576</td>\n",
+              "      <td>0.3784</td>\n",
+              "      <td>0.1932</td>\n",
+              "      <td>0.3063</td>\n",
+              "      <td>0.08368</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>1</td>\n",
+              "      <td>13.71</td>\n",
+              "      <td>20.83</td>\n",
+              "      <td>90.20</td>\n",
+              "      <td>577.9</td>\n",
+              "      <td>0.11890</td>\n",
+              "      <td>0.16450</td>\n",
+              "      <td>0.09366</td>\n",
+              "      <td>0.05985</td>\n",
+              "      <td>0.2196</td>\n",
+              "      <td>...</td>\n",
+              "      <td>28.14</td>\n",
+              "      <td>110.60</td>\n",
+              "      <td>897.0</td>\n",
+              "      <td>0.1654</td>\n",
+              "      <td>0.3682</td>\n",
+              "      <td>0.2678</td>\n",
+              "      <td>0.1556</td>\n",
+              "      <td>0.3196</td>\n",
+              "      <td>0.11510</td>\n",
+              "      <td>-1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>1</td>\n",
+              "      <td>13.00</td>\n",
+              "      <td>21.82</td>\n",
+              "      <td>87.50</td>\n",
+              "      <td>519.8</td>\n",
+              "      <td>0.12730</td>\n",
+              "      <td>0.19320</td>\n",
+              "      <td>0.18590</td>\n",
+              "      <td>0.09353</td>\n",
+              "      <td>0.2350</td>\n",
+              "      <td>...</td>\n",
+              "      <td>30.73</td>\n",
+              "      <td>106.20</td>\n",
+              "      <td>739.3</td>\n",
+              "      <td>0.1703</td>\n",
+              "      <td>0.5401</td>\n",
+              "      <td>0.5390</td>\n",
+              "      <td>0.2060</td>\n",
+              "      <td>0.4378</td>\n",
+              "      <td>0.10720</td>\n",
+              "      <td>-1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>1</td>\n",
+              "      <td>12.46</td>\n",
+              "      <td>24.04</td>\n",
+              "      <td>83.97</td>\n",
+              "      <td>475.9</td>\n",
+              "      <td>0.11860</td>\n",
+              "      <td>0.23960</td>\n",
+              "      <td>0.22730</td>\n",
+              "      <td>0.08543</td>\n",
+              "      <td>0.2030</td>\n",
+              "      <td>...</td>\n",
+              "      <td>40.68</td>\n",
+              "      <td>97.65</td>\n",
+              "      <td>711.4</td>\n",
+              "      <td>0.1853</td>\n",
+              "      <td>1.0580</td>\n",
+              "      <td>1.1050</td>\n",
+              "      <td>0.2210</td>\n",
+              "      <td>0.4366</td>\n",
+              "      <td>0.20750</td>\n",
+              "      <td>-1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>10 rows × 32 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cdf2a5af-29b0-43f6-baef-5196d49f08c3')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-cdf2a5af-29b0-43f6-baef-5196d49f08c3 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-cdf2a5af-29b0-43f6-baef-5196d49f08c3');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-85a8e093-d7e0-4b2f-a47a-44c5cc397605\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-85a8e093-d7e0-4b2f-a47a-44c5cc397605')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-85a8e093-d7e0-4b2f-a47a-44c5cc397605 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "🧩 Silhouette Score: 0.0368 (higher = better clustering)\n",
+            "\n",
+            "📊 Cluster Counts:\n",
+            "Cluster\n",
+            " 1    269\n",
+            "-1    231\n",
+            " 0     64\n",
+            " 2      5\n",
+            "Name: count, dtype: int64\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 127752 (\\N{RAINBOW}) missing from font(s) DejaVu Sans.\n",
+            "  fig.canvas.print_figure(bytes_io, **kw)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# =====================================\n",
+        "# 🧬 DBSCAN Clustering on Breast Cancer Dataset (Unsupervised Learning)\n",
+        "# =====================================\n",
+        "\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.cluster import DBSCAN\n",
+        "from sklearn.decomposition import PCA\n",
+        "from sklearn.metrics import silhouette_score\n",
+        "from google.colab import files\n",
+        "import io\n",
+        "\n",
+        "# Step 1️⃣: Upload CSV file\n",
+        "uploaded = files.upload()\n",
+        "csv_filename = list(uploaded.keys())[0]\n",
+        "\n",
+        "# Step 2️⃣: Load dataset\n",
+        "df = pd.read_csv(io.BytesIO(uploaded[csv_filename]))\n",
+        "print(\"✅ File Loaded Successfully!\\n\")\n",
+        "display(df.head())\n",
+        "\n",
+        "# Step 3️⃣: Clean column names\n",
+        "df.columns = df.columns.str.strip()\n",
+        "print(\"\\n📋 Columns in dataset:\")\n",
+        "print(df.columns.tolist())\n",
+        "\n",
+        "# Step 4️⃣: Data Preprocessing\n",
+        "# Drop 'id' or unnamed columns\n",
+        "df = df.drop(columns=[col for col in df.columns if 'id' in col.lower() or 'unnamed' in col.lower()], errors='ignore')\n",
+        "\n",
+        "# Convert 'diagnosis' to numeric for reference (not used for clustering)\n",
+        "if 'diagnosis' in df.columns:\n",
+        "    df['diagnosis'] = df['diagnosis'].map({'M': 1, 'B': 0})\n",
+        "\n",
+        "# Select only numeric columns\n",
+        "df_clean = df.select_dtypes(include=[np.number])\n",
+        "\n",
+        "# Remove NaN or infinite values\n",
+        "df_clean = df_clean.replace([np.inf, -np.inf], np.nan).dropna()\n",
+        "\n",
+        "# Step 5️⃣: Standardize the data\n",
+        "scaler = StandardScaler()\n",
+        "X_scaled = scaler.fit_transform(df_clean)\n",
+        "\n",
+        "# Step 6️⃣: Apply DBSCAN Clustering\n",
+        "dbscan = DBSCAN(eps=2.5, min_samples=5)  # you can tune eps\n",
+        "clusters = dbscan.fit_predict(X_scaled)\n",
+        "\n",
+        "# Step 7️⃣: Add cluster labels to DataFrame\n",
+        "df_clean['Cluster'] = clusters\n",
+        "display(df_clean.head(10))\n",
+        "\n",
+        "# Step 8️⃣: Evaluate clustering quality (only if >1 cluster)\n",
+        "unique_clusters = len(set(clusters)) - (1 if -1 in clusters else 0)\n",
+        "if unique_clusters > 1:\n",
+        "    sil_score = silhouette_score(X_scaled, clusters)\n",
+        "    print(f\"\\n🧩 Silhouette Score: {sil_score:.4f} (higher = better clustering)\")\n",
+        "else:\n",
+        "    print(\"\\n⚠️ Only one cluster found — silhouette score not applicable.\")\n",
+        "\n",
+        "# Step 9️⃣: Show cluster distribution\n",
+        "print(\"\\n📊 Cluster Counts:\")\n",
+        "print(df_clean['Cluster'].value_counts())\n",
+        "\n",
+        "# Step 🔟: Visualize Clusters (PCA 2D Projection)\n",
+        "pca = PCA(n_components=2)\n",
+        "X_pca = pca.fit_transform(X_scaled)\n",
+        "\n",
+        "plt.figure(figsize=(8,6))\n",
+        "sns.scatterplot(x=X_pca[:, 0], y=X_pca[:, 1], hue=clusters, palette='rainbow', legend='full')\n",
+        "plt.title(\"🌈 DBSCAN Clustering (PCA Visualization)\")\n",
+        "plt.xlabel(\"Principal Component 1\")\n",
+        "plt.ylabel(\"Principal Component 2\")\n",
+        "plt.legend(title=\"Cluster\")\n",
+        "plt.show()\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/credit-card-fraud-detection/README.md b/credit-card-fraud-detection/README.md
new file mode 100644
index 0000000..bb64674
--- /dev/null
+++ b/credit-card-fraud-detection/README.md
@@ -0,0 +1,31 @@
+# 💳 Credit Card Fraud Detection using DBSCAN Clustering
+
+An unsupervised machine learning approach to detect fraudulent credit 
+card transactions using DBSCAN clustering algorithm.
+
+## 🎯 Overview
+Implements an unsupervised anomaly detection system for identifying 
+potentially fraudulent credit card transactions without labeled data.
+
+## 🚀 Installation
+```bash
+pip install pandas numpy matplotlib scikit-learn
+```
+
+## 💻 Usage
+Open `DBSCAN_clustering.ipynb` in Jupyter Notebook or Google Colab 
+and run all cells sequentially.
+
+## 📊 Dataset
+Works with credit card transaction datasets. Recommended:
+[Kaggle Credit Card Fraud Detection Dataset](https://www.kaggle.com/mlg-ulb/creditcardfraud)
+
+## 🔬 Methodology
+1. Data Preprocessing — handles missing values and standardizes features
+2. DBSCAN Clustering — density-based anomaly detection
+3. Anomaly Detection — points labeled -1 are potential fraud
+4. PCA Visualization — 2D projection of clusters
+
+## 📚 References
+- Ester, M., et al. (1996). "A density-based algorithm for discovering clusters"
+- Scikit-learn DBSCAN Documentation
\ No newline at end of file
diff --git a/credit-card-fraud-detection/requirements.txt b/credit-card-fraud-detection/requirements.txt
new file mode 100644
index 0000000..c596dd4
--- /dev/null
+++ b/credit-card-fraud-detection/requirements.txt
@@ -0,0 +1,4 @@
+pandas
+numpy
+matplotlib
+scikit-learn

From 7ed130a0264d6449d964bfc7c17067019a2b5059 Mon Sep 17 00:00:00 2001
From: zain-cs <mzainulabidin.cs@gmail.com>
Date: Wed, 24 Jun 2026 23:47:48 +0500
Subject: [PATCH 2/3] fix: import display from IPython to resolve lint errors

---
 .../DBSCAN_clustering.ipynb                   | 281 +++++++++---------
 1 file changed, 141 insertions(+), 140 deletions(-)

diff --git a/credit-card-fraud-detection/DBSCAN_clustering.ipynb b/credit-card-fraud-detection/DBSCAN_clustering.ipynb
index 54f0773..f140a15 100644
--- a/credit-card-fraud-detection/DBSCAN_clustering.ipynb
+++ b/credit-card-fraud-detection/DBSCAN_clustering.ipynb
@@ -1,18 +1,4 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
   "cells": [
     {
       "cell_type": "code",
@@ -27,11 +13,7 @@
       },
       "outputs": [
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
             "text/html": [
               "\n",
               "     <input type=\"file\" id=\"files-3a06c761-ca18-4e78-9fb2-70845db888aa\" name=\"files[]\" multiple disabled\n",
@@ -216,13 +198,17 @@
               "};\n",
               "})(self);\n",
               "</script> "
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
             ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Saving Breast_cancer_dataset (1).csv to Breast_cancer_dataset (1) (1).csv\n",
             "✅ File Loaded Successfully!\n",
@@ -230,46 +216,10 @@
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
-              "0    842302         M        17.99         10.38          122.80     1001.0   \n",
-              "1    842517         M        20.57         17.77          132.90     1326.0   \n",
-              "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
-              "3  84348301         M        11.42         20.38           77.58      386.1   \n",
-              "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
-              "\n",
-              "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
-              "0          0.11840           0.27760          0.3001              0.14710   \n",
-              "1          0.08474           0.07864          0.0869              0.07017   \n",
-              "2          0.10960           0.15990          0.1974              0.12790   \n",
-              "3          0.14250           0.28390          0.2414              0.10520   \n",
-              "4          0.10030           0.13280          0.1980              0.10430   \n",
-              "\n",
-              "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
-              "0  ...          17.33           184.60      2019.0            0.1622   \n",
-              "1  ...          23.41           158.80      1956.0            0.1238   \n",
-              "2  ...          25.53           152.50      1709.0            0.1444   \n",
-              "3  ...          26.50            98.87       567.7            0.2098   \n",
-              "4  ...          16.67           152.20      1575.0            0.1374   \n",
-              "\n",
-              "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
-              "0             0.6656           0.7119                0.2654          0.4601   \n",
-              "1             0.1866           0.2416                0.1860          0.2750   \n",
-              "2             0.4245           0.4504                0.2430          0.3613   \n",
-              "3             0.8663           0.6869                0.2575          0.6638   \n",
-              "4             0.2050           0.4000                0.1625          0.2364   \n",
-              "\n",
-              "   fractal_dimension_worst  Unnamed: 32  \n",
-              "0                  0.11890          NaN  \n",
-              "1                  0.08902          NaN  \n",
-              "2                  0.08758          NaN  \n",
-              "3                  0.17300          NaN  \n",
-              "4                  0.07678          NaN  \n",
-              "\n",
-              "[5 rows x 33 columns]"
-            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe"
+            },
             "text/html": [
               "\n",
               "  <div id=\"df-e755260c-f509-4714-8c79-961c52e72b18\" class=\"colab-df-container\">\n",
@@ -649,15 +599,51 @@
               "    </div>\n",
               "  </div>\n"
             ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe"
-            }
+            "text/plain": [
+              "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
+              "0    842302         M        17.99         10.38          122.80     1001.0   \n",
+              "1    842517         M        20.57         17.77          132.90     1326.0   \n",
+              "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
+              "3  84348301         M        11.42         20.38           77.58      386.1   \n",
+              "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
+              "\n",
+              "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
+              "0          0.11840           0.27760          0.3001              0.14710   \n",
+              "1          0.08474           0.07864          0.0869              0.07017   \n",
+              "2          0.10960           0.15990          0.1974              0.12790   \n",
+              "3          0.14250           0.28390          0.2414              0.10520   \n",
+              "4          0.10030           0.13280          0.1980              0.10430   \n",
+              "\n",
+              "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
+              "0  ...          17.33           184.60      2019.0            0.1622   \n",
+              "1  ...          23.41           158.80      1956.0            0.1238   \n",
+              "2  ...          25.53           152.50      1709.0            0.1444   \n",
+              "3  ...          26.50            98.87       567.7            0.2098   \n",
+              "4  ...          16.67           152.20      1575.0            0.1374   \n",
+              "\n",
+              "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
+              "0             0.6656           0.7119                0.2654          0.4601   \n",
+              "1             0.1866           0.2416                0.1860          0.2750   \n",
+              "2             0.4245           0.4504                0.2430          0.3613   \n",
+              "3             0.8663           0.6869                0.2575          0.6638   \n",
+              "4             0.2050           0.4000                0.1625          0.2364   \n",
+              "\n",
+              "   fractal_dimension_worst  Unnamed: 32  \n",
+              "0                  0.11890          NaN  \n",
+              "1                  0.08902          NaN  \n",
+              "2                  0.08758          NaN  \n",
+              "3                  0.17300          NaN  \n",
+              "4                  0.07678          NaN  \n",
+              "\n",
+              "[5 rows x 33 columns]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "\n",
             "📋 Columns in dataset:\n",
@@ -665,71 +651,10 @@
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "   diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
-              "0          1        17.99         10.38          122.80     1001.0   \n",
-              "1          1        20.57         17.77          132.90     1326.0   \n",
-              "2          1        19.69         21.25          130.00     1203.0   \n",
-              "3          1        11.42         20.38           77.58      386.1   \n",
-              "4          1        20.29         14.34          135.10     1297.0   \n",
-              "5          1        12.45         15.70           82.57      477.1   \n",
-              "6          1        18.25         19.98          119.60     1040.0   \n",
-              "7          1        13.71         20.83           90.20      577.9   \n",
-              "8          1        13.00         21.82           87.50      519.8   \n",
-              "9          1        12.46         24.04           83.97      475.9   \n",
-              "\n",
-              "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
-              "0          0.11840           0.27760         0.30010              0.14710   \n",
-              "1          0.08474           0.07864         0.08690              0.07017   \n",
-              "2          0.10960           0.15990         0.19740              0.12790   \n",
-              "3          0.14250           0.28390         0.24140              0.10520   \n",
-              "4          0.10030           0.13280         0.19800              0.10430   \n",
-              "5          0.12780           0.17000         0.15780              0.08089   \n",
-              "6          0.09463           0.10900         0.11270              0.07400   \n",
-              "7          0.11890           0.16450         0.09366              0.05985   \n",
-              "8          0.12730           0.19320         0.18590              0.09353   \n",
-              "9          0.11860           0.23960         0.22730              0.08543   \n",
-              "\n",
-              "   symmetry_mean  ...  texture_worst  perimeter_worst  area_worst  \\\n",
-              "0         0.2419  ...          17.33           184.60      2019.0   \n",
-              "1         0.1812  ...          23.41           158.80      1956.0   \n",
-              "2         0.2069  ...          25.53           152.50      1709.0   \n",
-              "3         0.2597  ...          26.50            98.87       567.7   \n",
-              "4         0.1809  ...          16.67           152.20      1575.0   \n",
-              "5         0.2087  ...          23.75           103.40       741.6   \n",
-              "6         0.1794  ...          27.66           153.20      1606.0   \n",
-              "7         0.2196  ...          28.14           110.60       897.0   \n",
-              "8         0.2350  ...          30.73           106.20       739.3   \n",
-              "9         0.2030  ...          40.68            97.65       711.4   \n",
-              "\n",
-              "   smoothness_worst  compactness_worst  concavity_worst  concave points_worst  \\\n",
-              "0            0.1622             0.6656           0.7119                0.2654   \n",
-              "1            0.1238             0.1866           0.2416                0.1860   \n",
-              "2            0.1444             0.4245           0.4504                0.2430   \n",
-              "3            0.2098             0.8663           0.6869                0.2575   \n",
-              "4            0.1374             0.2050           0.4000                0.1625   \n",
-              "5            0.1791             0.5249           0.5355                0.1741   \n",
-              "6            0.1442             0.2576           0.3784                0.1932   \n",
-              "7            0.1654             0.3682           0.2678                0.1556   \n",
-              "8            0.1703             0.5401           0.5390                0.2060   \n",
-              "9            0.1853             1.0580           1.1050                0.2210   \n",
-              "\n",
-              "   symmetry_worst  fractal_dimension_worst  Cluster  \n",
-              "0          0.4601                  0.11890       -1  \n",
-              "1          0.2750                  0.08902        0  \n",
-              "2          0.3613                  0.08758       -1  \n",
-              "3          0.6638                  0.17300       -1  \n",
-              "4          0.2364                  0.07678       -1  \n",
-              "5          0.3985                  0.12440       -1  \n",
-              "6          0.3063                  0.08368        0  \n",
-              "7          0.3196                  0.11510       -1  \n",
-              "8          0.4378                  0.10720       -1  \n",
-              "9          0.4366                  0.20750       -1  \n",
-              "\n",
-              "[10 rows x 32 columns]"
-            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe"
+            },
             "text/html": [
               "\n",
               "  <div id=\"df-cdf2a5af-29b0-43f6-baef-5196d49f08c3\" class=\"colab-df-container\">\n",
@@ -1229,15 +1154,76 @@
               "    </div>\n",
               "  </div>\n"
             ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe"
-            }
+            "text/plain": [
+              "   diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
+              "0          1        17.99         10.38          122.80     1001.0   \n",
+              "1          1        20.57         17.77          132.90     1326.0   \n",
+              "2          1        19.69         21.25          130.00     1203.0   \n",
+              "3          1        11.42         20.38           77.58      386.1   \n",
+              "4          1        20.29         14.34          135.10     1297.0   \n",
+              "5          1        12.45         15.70           82.57      477.1   \n",
+              "6          1        18.25         19.98          119.60     1040.0   \n",
+              "7          1        13.71         20.83           90.20      577.9   \n",
+              "8          1        13.00         21.82           87.50      519.8   \n",
+              "9          1        12.46         24.04           83.97      475.9   \n",
+              "\n",
+              "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
+              "0          0.11840           0.27760         0.30010              0.14710   \n",
+              "1          0.08474           0.07864         0.08690              0.07017   \n",
+              "2          0.10960           0.15990         0.19740              0.12790   \n",
+              "3          0.14250           0.28390         0.24140              0.10520   \n",
+              "4          0.10030           0.13280         0.19800              0.10430   \n",
+              "5          0.12780           0.17000         0.15780              0.08089   \n",
+              "6          0.09463           0.10900         0.11270              0.07400   \n",
+              "7          0.11890           0.16450         0.09366              0.05985   \n",
+              "8          0.12730           0.19320         0.18590              0.09353   \n",
+              "9          0.11860           0.23960         0.22730              0.08543   \n",
+              "\n",
+              "   symmetry_mean  ...  texture_worst  perimeter_worst  area_worst  \\\n",
+              "0         0.2419  ...          17.33           184.60      2019.0   \n",
+              "1         0.1812  ...          23.41           158.80      1956.0   \n",
+              "2         0.2069  ...          25.53           152.50      1709.0   \n",
+              "3         0.2597  ...          26.50            98.87       567.7   \n",
+              "4         0.1809  ...          16.67           152.20      1575.0   \n",
+              "5         0.2087  ...          23.75           103.40       741.6   \n",
+              "6         0.1794  ...          27.66           153.20      1606.0   \n",
+              "7         0.2196  ...          28.14           110.60       897.0   \n",
+              "8         0.2350  ...          30.73           106.20       739.3   \n",
+              "9         0.2030  ...          40.68            97.65       711.4   \n",
+              "\n",
+              "   smoothness_worst  compactness_worst  concavity_worst  concave points_worst  \\\n",
+              "0            0.1622             0.6656           0.7119                0.2654   \n",
+              "1            0.1238             0.1866           0.2416                0.1860   \n",
+              "2            0.1444             0.4245           0.4504                0.2430   \n",
+              "3            0.2098             0.8663           0.6869                0.2575   \n",
+              "4            0.1374             0.2050           0.4000                0.1625   \n",
+              "5            0.1791             0.5249           0.5355                0.1741   \n",
+              "6            0.1442             0.2576           0.3784                0.1932   \n",
+              "7            0.1654             0.3682           0.2678                0.1556   \n",
+              "8            0.1703             0.5401           0.5390                0.2060   \n",
+              "9            0.1853             1.0580           1.1050                0.2210   \n",
+              "\n",
+              "   symmetry_worst  fractal_dimension_worst  Cluster  \n",
+              "0          0.4601                  0.11890       -1  \n",
+              "1          0.2750                  0.08902        0  \n",
+              "2          0.3613                  0.08758       -1  \n",
+              "3          0.6638                  0.17300       -1  \n",
+              "4          0.2364                  0.07678       -1  \n",
+              "5          0.3985                  0.12440       -1  \n",
+              "6          0.3063                  0.08368        0  \n",
+              "7          0.3196                  0.11510       -1  \n",
+              "8          0.4378                  0.10720       -1  \n",
+              "9          0.4366                  0.20750       -1  \n",
+              "\n",
+              "[10 rows x 32 columns]"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         },
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "\n",
             "🧩 Silhouette Score: 0.0368 (higher = better clustering)\n",
@@ -1252,22 +1238,22 @@
           ]
         },
         {
-          "output_type": "stream",
           "name": "stderr",
+          "output_type": "stream",
           "text": [
             "/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 127752 (\\N{RAINBOW}) missing from font(s) DejaVu Sans.\n",
             "  fig.canvas.print_figure(bytes_io, **kw)\n"
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
+            "image/png": "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",
             "text/plain": [
               "<Figure size 800x600 with 1 Axes>"
-            ],
-            "image/png": "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\n"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
       ],
       "source": [
@@ -1285,6 +1271,7 @@
         "from sklearn.metrics import silhouette_score\n",
         "from google.colab import files\n",
         "import io\n",
+        "from IPython.display import display\n",
         "\n",
         "# Step 1️⃣: Upload CSV file\n",
         "uploaded = files.upload()\n",
@@ -1351,5 +1338,19 @@
         "plt.show()\n"
       ]
     }
-  ]
-}
\ No newline at end of file
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

From 14e60db0f83d81ad79502faa7a6f9ac57c469e60 Mon Sep 17 00:00:00 2001
From: zain-cs <mzainulabidin.cs@gmail.com>
Date: Thu, 25 Jun 2026 00:03:43 +0500
Subject: [PATCH 3/3] fix: replace with correct Credit Card Fraud Detection
 notebook

---
 .../DBSCAN_clustering.ipynb                   | 974 +++++++++---------
 1 file changed, 481 insertions(+), 493 deletions(-)

diff --git a/credit-card-fraud-detection/DBSCAN_clustering.ipynb b/credit-card-fraud-detection/DBSCAN_clustering.ipynb
index f140a15..036a647 100644
--- a/credit-card-fraud-detection/DBSCAN_clustering.ipynb
+++ b/credit-card-fraud-detection/DBSCAN_clustering.ipynb
@@ -5,20 +5,21 @@
       "execution_count": null,
       "metadata": {
         "colab": {
+          "background_save": true,
           "base_uri": "https://localhost:8080/",
           "height": 1000
         },
         "id": "6vPIHEvB5ggH",
-        "outputId": "8de9878e-fdd1-4bf1-fdd3-d783dd4d3ed9"
+        "outputId": "f01b194e-2221-4083-e020-a667c175cdfc"
       },
       "outputs": [
         {
           "data": {
             "text/html": [
               "\n",
-              "     <input type=\"file\" id=\"files-3a06c761-ca18-4e78-9fb2-70845db888aa\" name=\"files[]\" multiple disabled\n",
+              "     <input type=\"file\" id=\"files-c64ffd57-8637-4fe1-8773-a8c3c2dad2ba\" name=\"files[]\" multiple disabled\n",
               "        style=\"border:none\" />\n",
-              "     <output id=\"result-3a06c761-ca18-4e78-9fb2-70845db888aa\">\n",
+              "     <output id=\"result-c64ffd57-8637-4fe1-8773-a8c3c2dad2ba\">\n",
               "      Upload widget is only available when the cell has been executed in the\n",
               "      current browser session. Please rerun this cell to enable.\n",
               "      </output>\n",
@@ -210,8 +211,8 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Saving Breast_cancer_dataset (1).csv to Breast_cancer_dataset (1) (1).csv\n",
-            "✅ File Loaded Successfully!\n",
+            "Saving creditcard.csv to creditcard.csv\n",
+            "✅ Credit Card Dataset Loaded Successfully!\n",
             "\n"
           ]
         },
@@ -222,7 +223,7 @@
             },
             "text/html": [
               "\n",
-              "  <div id=\"df-e755260c-f509-4714-8c79-961c52e72b18\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-a4f0efab-1522-46af-b817-ec0d9f8c48b1\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -241,158 +242,158 @@
               "  <thead>\n",
               "    <tr style=\"text-align: right;\">\n",
               "      <th></th>\n",
-              "      <th>id</th>\n",
-              "      <th>diagnosis</th>\n",
-              "      <th>radius_mean</th>\n",
-              "      <th>texture_mean</th>\n",
-              "      <th>perimeter_mean</th>\n",
-              "      <th>area_mean</th>\n",
-              "      <th>smoothness_mean</th>\n",
-              "      <th>compactness_mean</th>\n",
-              "      <th>concavity_mean</th>\n",
-              "      <th>concave points_mean</th>\n",
+              "      <th>Time</th>\n",
+              "      <th>V1</th>\n",
+              "      <th>V2</th>\n",
+              "      <th>V3</th>\n",
+              "      <th>V4</th>\n",
+              "      <th>V5</th>\n",
+              "      <th>V6</th>\n",
+              "      <th>V7</th>\n",
+              "      <th>V8</th>\n",
+              "      <th>V9</th>\n",
               "      <th>...</th>\n",
-              "      <th>texture_worst</th>\n",
-              "      <th>perimeter_worst</th>\n",
-              "      <th>area_worst</th>\n",
-              "      <th>smoothness_worst</th>\n",
-              "      <th>compactness_worst</th>\n",
-              "      <th>concavity_worst</th>\n",
-              "      <th>concave points_worst</th>\n",
-              "      <th>symmetry_worst</th>\n",
-              "      <th>fractal_dimension_worst</th>\n",
-              "      <th>Unnamed: 32</th>\n",
+              "      <th>V21</th>\n",
+              "      <th>V22</th>\n",
+              "      <th>V23</th>\n",
+              "      <th>V24</th>\n",
+              "      <th>V25</th>\n",
+              "      <th>V26</th>\n",
+              "      <th>V27</th>\n",
+              "      <th>V28</th>\n",
+              "      <th>Amount</th>\n",
+              "      <th>Class</th>\n",
               "    </tr>\n",
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
               "      <th>0</th>\n",
-              "      <td>842302</td>\n",
-              "      <td>M</td>\n",
-              "      <td>17.99</td>\n",
-              "      <td>10.38</td>\n",
-              "      <td>122.80</td>\n",
-              "      <td>1001.0</td>\n",
-              "      <td>0.11840</td>\n",
-              "      <td>0.27760</td>\n",
-              "      <td>0.3001</td>\n",
-              "      <td>0.14710</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-1.359807</td>\n",
+              "      <td>-0.072781</td>\n",
+              "      <td>2.536347</td>\n",
+              "      <td>1.378155</td>\n",
+              "      <td>-0.338321</td>\n",
+              "      <td>0.462388</td>\n",
+              "      <td>0.239599</td>\n",
+              "      <td>0.098698</td>\n",
+              "      <td>0.363787</td>\n",
               "      <td>...</td>\n",
-              "      <td>17.33</td>\n",
-              "      <td>184.60</td>\n",
-              "      <td>2019.0</td>\n",
-              "      <td>0.1622</td>\n",
-              "      <td>0.6656</td>\n",
-              "      <td>0.7119</td>\n",
-              "      <td>0.2654</td>\n",
-              "      <td>0.4601</td>\n",
-              "      <td>0.11890</td>\n",
-              "      <td>NaN</td>\n",
+              "      <td>-0.018307</td>\n",
+              "      <td>0.277838</td>\n",
+              "      <td>-0.110474</td>\n",
+              "      <td>0.066928</td>\n",
+              "      <td>0.128539</td>\n",
+              "      <td>-0.189115</td>\n",
+              "      <td>0.133558</td>\n",
+              "      <td>-0.021053</td>\n",
+              "      <td>149.62</td>\n",
+              "      <td>0</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1</th>\n",
-              "      <td>842517</td>\n",
-              "      <td>M</td>\n",
-              "      <td>20.57</td>\n",
-              "      <td>17.77</td>\n",
-              "      <td>132.90</td>\n",
-              "      <td>1326.0</td>\n",
-              "      <td>0.08474</td>\n",
-              "      <td>0.07864</td>\n",
-              "      <td>0.0869</td>\n",
-              "      <td>0.07017</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.191857</td>\n",
+              "      <td>0.266151</td>\n",
+              "      <td>0.166480</td>\n",
+              "      <td>0.448154</td>\n",
+              "      <td>0.060018</td>\n",
+              "      <td>-0.082361</td>\n",
+              "      <td>-0.078803</td>\n",
+              "      <td>0.085102</td>\n",
+              "      <td>-0.255425</td>\n",
               "      <td>...</td>\n",
-              "      <td>23.41</td>\n",
-              "      <td>158.80</td>\n",
-              "      <td>1956.0</td>\n",
-              "      <td>0.1238</td>\n",
-              "      <td>0.1866</td>\n",
-              "      <td>0.2416</td>\n",
-              "      <td>0.1860</td>\n",
-              "      <td>0.2750</td>\n",
-              "      <td>0.08902</td>\n",
-              "      <td>NaN</td>\n",
+              "      <td>-0.225775</td>\n",
+              "      <td>-0.638672</td>\n",
+              "      <td>0.101288</td>\n",
+              "      <td>-0.339846</td>\n",
+              "      <td>0.167170</td>\n",
+              "      <td>0.125895</td>\n",
+              "      <td>-0.008983</td>\n",
+              "      <td>0.014724</td>\n",
+              "      <td>2.69</td>\n",
+              "      <td>0</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>2</th>\n",
-              "      <td>84300903</td>\n",
-              "      <td>M</td>\n",
-              "      <td>19.69</td>\n",
-              "      <td>21.25</td>\n",
-              "      <td>130.00</td>\n",
-              "      <td>1203.0</td>\n",
-              "      <td>0.10960</td>\n",
-              "      <td>0.15990</td>\n",
-              "      <td>0.1974</td>\n",
-              "      <td>0.12790</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-1.358354</td>\n",
+              "      <td>-1.340163</td>\n",
+              "      <td>1.773209</td>\n",
+              "      <td>0.379780</td>\n",
+              "      <td>-0.503198</td>\n",
+              "      <td>1.800499</td>\n",
+              "      <td>0.791461</td>\n",
+              "      <td>0.247676</td>\n",
+              "      <td>-1.514654</td>\n",
               "      <td>...</td>\n",
-              "      <td>25.53</td>\n",
-              "      <td>152.50</td>\n",
-              "      <td>1709.0</td>\n",
-              "      <td>0.1444</td>\n",
-              "      <td>0.4245</td>\n",
-              "      <td>0.4504</td>\n",
-              "      <td>0.2430</td>\n",
-              "      <td>0.3613</td>\n",
-              "      <td>0.08758</td>\n",
-              "      <td>NaN</td>\n",
+              "      <td>0.247998</td>\n",
+              "      <td>0.771679</td>\n",
+              "      <td>0.909412</td>\n",
+              "      <td>-0.689281</td>\n",
+              "      <td>-0.327642</td>\n",
+              "      <td>-0.139097</td>\n",
+              "      <td>-0.055353</td>\n",
+              "      <td>-0.059752</td>\n",
+              "      <td>378.66</td>\n",
+              "      <td>0</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>3</th>\n",
-              "      <td>84348301</td>\n",
-              "      <td>M</td>\n",
-              "      <td>11.42</td>\n",
-              "      <td>20.38</td>\n",
-              "      <td>77.58</td>\n",
-              "      <td>386.1</td>\n",
-              "      <td>0.14250</td>\n",
-              "      <td>0.28390</td>\n",
-              "      <td>0.2414</td>\n",
-              "      <td>0.10520</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.966272</td>\n",
+              "      <td>-0.185226</td>\n",
+              "      <td>1.792993</td>\n",
+              "      <td>-0.863291</td>\n",
+              "      <td>-0.010309</td>\n",
+              "      <td>1.247203</td>\n",
+              "      <td>0.237609</td>\n",
+              "      <td>0.377436</td>\n",
+              "      <td>-1.387024</td>\n",
               "      <td>...</td>\n",
-              "      <td>26.50</td>\n",
-              "      <td>98.87</td>\n",
-              "      <td>567.7</td>\n",
-              "      <td>0.2098</td>\n",
-              "      <td>0.8663</td>\n",
-              "      <td>0.6869</td>\n",
-              "      <td>0.2575</td>\n",
-              "      <td>0.6638</td>\n",
-              "      <td>0.17300</td>\n",
-              "      <td>NaN</td>\n",
+              "      <td>-0.108300</td>\n",
+              "      <td>0.005274</td>\n",
+              "      <td>-0.190321</td>\n",
+              "      <td>-1.175575</td>\n",
+              "      <td>0.647376</td>\n",
+              "      <td>-0.221929</td>\n",
+              "      <td>0.062723</td>\n",
+              "      <td>0.061458</td>\n",
+              "      <td>123.50</td>\n",
+              "      <td>0</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>4</th>\n",
-              "      <td>84358402</td>\n",
-              "      <td>M</td>\n",
-              "      <td>20.29</td>\n",
-              "      <td>14.34</td>\n",
-              "      <td>135.10</td>\n",
-              "      <td>1297.0</td>\n",
-              "      <td>0.10030</td>\n",
-              "      <td>0.13280</td>\n",
-              "      <td>0.1980</td>\n",
-              "      <td>0.10430</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>-1.158233</td>\n",
+              "      <td>0.877737</td>\n",
+              "      <td>1.548718</td>\n",
+              "      <td>0.403034</td>\n",
+              "      <td>-0.407193</td>\n",
+              "      <td>0.095921</td>\n",
+              "      <td>0.592941</td>\n",
+              "      <td>-0.270533</td>\n",
+              "      <td>0.817739</td>\n",
               "      <td>...</td>\n",
-              "      <td>16.67</td>\n",
-              "      <td>152.20</td>\n",
-              "      <td>1575.0</td>\n",
-              "      <td>0.1374</td>\n",
-              "      <td>0.2050</td>\n",
-              "      <td>0.4000</td>\n",
-              "      <td>0.1625</td>\n",
-              "      <td>0.2364</td>\n",
-              "      <td>0.07678</td>\n",
-              "      <td>NaN</td>\n",
+              "      <td>-0.009431</td>\n",
+              "      <td>0.798278</td>\n",
+              "      <td>-0.137458</td>\n",
+              "      <td>0.141267</td>\n",
+              "      <td>-0.206010</td>\n",
+              "      <td>0.502292</td>\n",
+              "      <td>0.219422</td>\n",
+              "      <td>0.215153</td>\n",
+              "      <td>69.99</td>\n",
+              "      <td>0</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
-              "<p>5 rows × 33 columns</p>\n",
+              "<p>5 rows × 31 columns</p>\n",
               "</div>\n",
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e755260c-f509-4714-8c79-961c52e72b18')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a4f0efab-1522-46af-b817-ec0d9f8c48b1')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -444,12 +445,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-e755260c-f509-4714-8c79-961c52e72b18 button.colab-df-convert');\n",
+              "        document.querySelector('#df-a4f0efab-1522-46af-b817-ec0d9f8c48b1 button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-e755260c-f509-4714-8c79-961c52e72b18');\n",
+              "        const element = document.querySelector('#df-a4f0efab-1522-46af-b817-ec0d9f8c48b1');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -469,8 +470,8 @@
               "  </div>\n",
               "\n",
               "\n",
-              "    <div id=\"df-6b324c6e-8f4c-4c72-94db-78ea1626a4ff\">\n",
-              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6b324c6e-8f4c-4c72-94db-78ea1626a4ff')\"\n",
+              "    <div id=\"df-3436c023-72f8-4173-988b-d424f965a968\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3436c023-72f8-4173-988b-d424f965a968')\"\n",
               "                title=\"Suggest charts\"\n",
               "                style=\"display:none;\">\n",
               "\n",
@@ -589,7 +590,7 @@
               "        }\n",
               "        (() => {\n",
               "          let quickchartButtonEl =\n",
-              "            document.querySelector('#df-6b324c6e-8f4c-4c72-94db-78ea1626a4ff button');\n",
+              "            document.querySelector('#df-3436c023-72f8-4173-988b-d424f965a968 button');\n",
               "          quickchartButtonEl.style.display =\n",
               "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "        })();\n",
@@ -600,42 +601,28 @@
               "  </div>\n"
             ],
             "text/plain": [
-              "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
-              "0    842302         M        17.99         10.38          122.80     1001.0   \n",
-              "1    842517         M        20.57         17.77          132.90     1326.0   \n",
-              "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
-              "3  84348301         M        11.42         20.38           77.58      386.1   \n",
-              "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
-              "\n",
-              "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
-              "0          0.11840           0.27760          0.3001              0.14710   \n",
-              "1          0.08474           0.07864          0.0869              0.07017   \n",
-              "2          0.10960           0.15990          0.1974              0.12790   \n",
-              "3          0.14250           0.28390          0.2414              0.10520   \n",
-              "4          0.10030           0.13280          0.1980              0.10430   \n",
-              "\n",
-              "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
-              "0  ...          17.33           184.60      2019.0            0.1622   \n",
-              "1  ...          23.41           158.80      1956.0            0.1238   \n",
-              "2  ...          25.53           152.50      1709.0            0.1444   \n",
-              "3  ...          26.50            98.87       567.7            0.2098   \n",
-              "4  ...          16.67           152.20      1575.0            0.1374   \n",
-              "\n",
-              "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
-              "0             0.6656           0.7119                0.2654          0.4601   \n",
-              "1             0.1866           0.2416                0.1860          0.2750   \n",
-              "2             0.4245           0.4504                0.2430          0.3613   \n",
-              "3             0.8663           0.6869                0.2575          0.6638   \n",
-              "4             0.2050           0.4000                0.1625          0.2364   \n",
-              "\n",
-              "   fractal_dimension_worst  Unnamed: 32  \n",
-              "0                  0.11890          NaN  \n",
-              "1                  0.08902          NaN  \n",
-              "2                  0.08758          NaN  \n",
-              "3                  0.17300          NaN  \n",
-              "4                  0.07678          NaN  \n",
-              "\n",
-              "[5 rows x 33 columns]"
+              "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
+              "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
+              "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
+              "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
+              "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
+              "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
+              "\n",
+              "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
+              "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
+              "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
+              "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
+              "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
+              "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
+              "\n",
+              "        V26       V27       V28  Amount  Class  \n",
+              "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
+              "1  0.125895 -0.008983  0.014724    2.69      0  \n",
+              "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
+              "3 -0.221929  0.062723  0.061458  123.50      0  \n",
+              "4  0.502292  0.219422  0.215153   69.99      0  \n",
+              "\n",
+              "[5 rows x 31 columns]"
             ]
           },
           "metadata": {},
@@ -647,7 +634,7 @@
           "text": [
             "\n",
             "📋 Columns in dataset:\n",
-            "['id', 'diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean', 'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean', 'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se', 'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se', 'fractal_dimension_se', 'radius_worst', 'texture_worst', 'perimeter_worst', 'area_worst', 'smoothness_worst', 'compactness_worst', 'concavity_worst', 'concave points_worst', 'symmetry_worst', 'fractal_dimension_worst', 'Unnamed: 32']\n"
+            "['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount', 'Class']\n"
           ]
         },
         {
@@ -657,7 +644,7 @@
             },
             "text/html": [
               "\n",
-              "  <div id=\"df-cdf2a5af-29b0-43f6-baef-5196d49f08c3\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-11a404c1-8452-43f4-9c08-43e28f6335e6\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -676,278 +663,278 @@
               "  <thead>\n",
               "    <tr style=\"text-align: right;\">\n",
               "      <th></th>\n",
-              "      <th>diagnosis</th>\n",
-              "      <th>radius_mean</th>\n",
-              "      <th>texture_mean</th>\n",
-              "      <th>perimeter_mean</th>\n",
-              "      <th>area_mean</th>\n",
-              "      <th>smoothness_mean</th>\n",
-              "      <th>compactness_mean</th>\n",
-              "      <th>concavity_mean</th>\n",
-              "      <th>concave points_mean</th>\n",
-              "      <th>symmetry_mean</th>\n",
+              "      <th>Time</th>\n",
+              "      <th>V1</th>\n",
+              "      <th>V2</th>\n",
+              "      <th>V3</th>\n",
+              "      <th>V4</th>\n",
+              "      <th>V5</th>\n",
+              "      <th>V6</th>\n",
+              "      <th>V7</th>\n",
+              "      <th>V8</th>\n",
+              "      <th>V9</th>\n",
               "      <th>...</th>\n",
-              "      <th>texture_worst</th>\n",
-              "      <th>perimeter_worst</th>\n",
-              "      <th>area_worst</th>\n",
-              "      <th>smoothness_worst</th>\n",
-              "      <th>compactness_worst</th>\n",
-              "      <th>concavity_worst</th>\n",
-              "      <th>concave points_worst</th>\n",
-              "      <th>symmetry_worst</th>\n",
-              "      <th>fractal_dimension_worst</th>\n",
+              "      <th>V21</th>\n",
+              "      <th>V22</th>\n",
+              "      <th>V23</th>\n",
+              "      <th>V24</th>\n",
+              "      <th>V25</th>\n",
+              "      <th>V26</th>\n",
+              "      <th>V27</th>\n",
+              "      <th>V28</th>\n",
+              "      <th>Amount</th>\n",
               "      <th>Cluster</th>\n",
               "    </tr>\n",
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
               "      <th>0</th>\n",
-              "      <td>1</td>\n",
-              "      <td>17.99</td>\n",
-              "      <td>10.38</td>\n",
-              "      <td>122.80</td>\n",
-              "      <td>1001.0</td>\n",
-              "      <td>0.11840</td>\n",
-              "      <td>0.27760</td>\n",
-              "      <td>0.30010</td>\n",
-              "      <td>0.14710</td>\n",
-              "      <td>0.2419</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-1.359807</td>\n",
+              "      <td>-0.072781</td>\n",
+              "      <td>2.536347</td>\n",
+              "      <td>1.378155</td>\n",
+              "      <td>-0.338321</td>\n",
+              "      <td>0.462388</td>\n",
+              "      <td>0.239599</td>\n",
+              "      <td>0.098698</td>\n",
+              "      <td>0.363787</td>\n",
               "      <td>...</td>\n",
-              "      <td>17.33</td>\n",
-              "      <td>184.60</td>\n",
-              "      <td>2019.0</td>\n",
-              "      <td>0.1622</td>\n",
-              "      <td>0.6656</td>\n",
-              "      <td>0.7119</td>\n",
-              "      <td>0.2654</td>\n",
-              "      <td>0.4601</td>\n",
-              "      <td>0.11890</td>\n",
+              "      <td>-0.018307</td>\n",
+              "      <td>0.277838</td>\n",
+              "      <td>-0.110474</td>\n",
+              "      <td>0.066928</td>\n",
+              "      <td>0.128539</td>\n",
+              "      <td>-0.189115</td>\n",
+              "      <td>0.133558</td>\n",
+              "      <td>-0.021053</td>\n",
+              "      <td>149.62</td>\n",
               "      <td>-1</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1</th>\n",
-              "      <td>1</td>\n",
-              "      <td>20.57</td>\n",
-              "      <td>17.77</td>\n",
-              "      <td>132.90</td>\n",
-              "      <td>1326.0</td>\n",
-              "      <td>0.08474</td>\n",
-              "      <td>0.07864</td>\n",
-              "      <td>0.08690</td>\n",
-              "      <td>0.07017</td>\n",
-              "      <td>0.1812</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.191857</td>\n",
+              "      <td>0.266151</td>\n",
+              "      <td>0.166480</td>\n",
+              "      <td>0.448154</td>\n",
+              "      <td>0.060018</td>\n",
+              "      <td>-0.082361</td>\n",
+              "      <td>-0.078803</td>\n",
+              "      <td>0.085102</td>\n",
+              "      <td>-0.255425</td>\n",
               "      <td>...</td>\n",
-              "      <td>23.41</td>\n",
-              "      <td>158.80</td>\n",
-              "      <td>1956.0</td>\n",
-              "      <td>0.1238</td>\n",
-              "      <td>0.1866</td>\n",
-              "      <td>0.2416</td>\n",
-              "      <td>0.1860</td>\n",
-              "      <td>0.2750</td>\n",
-              "      <td>0.08902</td>\n",
+              "      <td>-0.225775</td>\n",
+              "      <td>-0.638672</td>\n",
+              "      <td>0.101288</td>\n",
+              "      <td>-0.339846</td>\n",
+              "      <td>0.167170</td>\n",
+              "      <td>0.125895</td>\n",
+              "      <td>-0.008983</td>\n",
+              "      <td>0.014724</td>\n",
+              "      <td>2.69</td>\n",
               "      <td>0</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>2</th>\n",
-              "      <td>1</td>\n",
-              "      <td>19.69</td>\n",
-              "      <td>21.25</td>\n",
-              "      <td>130.00</td>\n",
-              "      <td>1203.0</td>\n",
-              "      <td>0.10960</td>\n",
-              "      <td>0.15990</td>\n",
-              "      <td>0.19740</td>\n",
-              "      <td>0.12790</td>\n",
-              "      <td>0.2069</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-1.358354</td>\n",
+              "      <td>-1.340163</td>\n",
+              "      <td>1.773209</td>\n",
+              "      <td>0.379780</td>\n",
+              "      <td>-0.503198</td>\n",
+              "      <td>1.800499</td>\n",
+              "      <td>0.791461</td>\n",
+              "      <td>0.247676</td>\n",
+              "      <td>-1.514654</td>\n",
               "      <td>...</td>\n",
-              "      <td>25.53</td>\n",
-              "      <td>152.50</td>\n",
-              "      <td>1709.0</td>\n",
-              "      <td>0.1444</td>\n",
-              "      <td>0.4245</td>\n",
-              "      <td>0.4504</td>\n",
-              "      <td>0.2430</td>\n",
-              "      <td>0.3613</td>\n",
-              "      <td>0.08758</td>\n",
+              "      <td>0.247998</td>\n",
+              "      <td>0.771679</td>\n",
+              "      <td>0.909412</td>\n",
+              "      <td>-0.689281</td>\n",
+              "      <td>-0.327642</td>\n",
+              "      <td>-0.139097</td>\n",
+              "      <td>-0.055353</td>\n",
+              "      <td>-0.059752</td>\n",
+              "      <td>378.66</td>\n",
               "      <td>-1</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>3</th>\n",
-              "      <td>1</td>\n",
-              "      <td>11.42</td>\n",
-              "      <td>20.38</td>\n",
-              "      <td>77.58</td>\n",
-              "      <td>386.1</td>\n",
-              "      <td>0.14250</td>\n",
-              "      <td>0.28390</td>\n",
-              "      <td>0.24140</td>\n",
-              "      <td>0.10520</td>\n",
-              "      <td>0.2597</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.966272</td>\n",
+              "      <td>-0.185226</td>\n",
+              "      <td>1.792993</td>\n",
+              "      <td>-0.863291</td>\n",
+              "      <td>-0.010309</td>\n",
+              "      <td>1.247203</td>\n",
+              "      <td>0.237609</td>\n",
+              "      <td>0.377436</td>\n",
+              "      <td>-1.387024</td>\n",
               "      <td>...</td>\n",
-              "      <td>26.50</td>\n",
-              "      <td>98.87</td>\n",
-              "      <td>567.7</td>\n",
-              "      <td>0.2098</td>\n",
-              "      <td>0.8663</td>\n",
-              "      <td>0.6869</td>\n",
-              "      <td>0.2575</td>\n",
-              "      <td>0.6638</td>\n",
-              "      <td>0.17300</td>\n",
+              "      <td>-0.108300</td>\n",
+              "      <td>0.005274</td>\n",
+              "      <td>-0.190321</td>\n",
+              "      <td>-1.175575</td>\n",
+              "      <td>0.647376</td>\n",
+              "      <td>-0.221929</td>\n",
+              "      <td>0.062723</td>\n",
+              "      <td>0.061458</td>\n",
+              "      <td>123.50</td>\n",
               "      <td>-1</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>4</th>\n",
-              "      <td>1</td>\n",
-              "      <td>20.29</td>\n",
-              "      <td>14.34</td>\n",
-              "      <td>135.10</td>\n",
-              "      <td>1297.0</td>\n",
-              "      <td>0.10030</td>\n",
-              "      <td>0.13280</td>\n",
-              "      <td>0.19800</td>\n",
-              "      <td>0.10430</td>\n",
-              "      <td>0.1809</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>-1.158233</td>\n",
+              "      <td>0.877737</td>\n",
+              "      <td>1.548718</td>\n",
+              "      <td>0.403034</td>\n",
+              "      <td>-0.407193</td>\n",
+              "      <td>0.095921</td>\n",
+              "      <td>0.592941</td>\n",
+              "      <td>-0.270533</td>\n",
+              "      <td>0.817739</td>\n",
               "      <td>...</td>\n",
-              "      <td>16.67</td>\n",
-              "      <td>152.20</td>\n",
-              "      <td>1575.0</td>\n",
-              "      <td>0.1374</td>\n",
-              "      <td>0.2050</td>\n",
-              "      <td>0.4000</td>\n",
-              "      <td>0.1625</td>\n",
-              "      <td>0.2364</td>\n",
-              "      <td>0.07678</td>\n",
+              "      <td>-0.009431</td>\n",
+              "      <td>0.798278</td>\n",
+              "      <td>-0.137458</td>\n",
+              "      <td>0.141267</td>\n",
+              "      <td>-0.206010</td>\n",
+              "      <td>0.502292</td>\n",
+              "      <td>0.219422</td>\n",
+              "      <td>0.215153</td>\n",
+              "      <td>69.99</td>\n",
               "      <td>-1</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>5</th>\n",
-              "      <td>1</td>\n",
-              "      <td>12.45</td>\n",
-              "      <td>15.70</td>\n",
-              "      <td>82.57</td>\n",
-              "      <td>477.1</td>\n",
-              "      <td>0.12780</td>\n",
-              "      <td>0.17000</td>\n",
-              "      <td>0.15780</td>\n",
-              "      <td>0.08089</td>\n",
-              "      <td>0.2087</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>-0.425966</td>\n",
+              "      <td>0.960523</td>\n",
+              "      <td>1.141109</td>\n",
+              "      <td>-0.168252</td>\n",
+              "      <td>0.420987</td>\n",
+              "      <td>-0.029728</td>\n",
+              "      <td>0.476201</td>\n",
+              "      <td>0.260314</td>\n",
+              "      <td>-0.568671</td>\n",
               "      <td>...</td>\n",
-              "      <td>23.75</td>\n",
-              "      <td>103.40</td>\n",
-              "      <td>741.6</td>\n",
-              "      <td>0.1791</td>\n",
-              "      <td>0.5249</td>\n",
-              "      <td>0.5355</td>\n",
-              "      <td>0.1741</td>\n",
-              "      <td>0.3985</td>\n",
-              "      <td>0.12440</td>\n",
-              "      <td>-1</td>\n",
+              "      <td>-0.208254</td>\n",
+              "      <td>-0.559825</td>\n",
+              "      <td>-0.026398</td>\n",
+              "      <td>-0.371427</td>\n",
+              "      <td>-0.232794</td>\n",
+              "      <td>0.105915</td>\n",
+              "      <td>0.253844</td>\n",
+              "      <td>0.081080</td>\n",
+              "      <td>3.67</td>\n",
+              "      <td>1</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>6</th>\n",
-              "      <td>1</td>\n",
-              "      <td>18.25</td>\n",
-              "      <td>19.98</td>\n",
-              "      <td>119.60</td>\n",
-              "      <td>1040.0</td>\n",
-              "      <td>0.09463</td>\n",
-              "      <td>0.10900</td>\n",
-              "      <td>0.11270</td>\n",
-              "      <td>0.07400</td>\n",
-              "      <td>0.1794</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>1.229658</td>\n",
+              "      <td>0.141004</td>\n",
+              "      <td>0.045371</td>\n",
+              "      <td>1.202613</td>\n",
+              "      <td>0.191881</td>\n",
+              "      <td>0.272708</td>\n",
+              "      <td>-0.005159</td>\n",
+              "      <td>0.081213</td>\n",
+              "      <td>0.464960</td>\n",
               "      <td>...</td>\n",
-              "      <td>27.66</td>\n",
-              "      <td>153.20</td>\n",
-              "      <td>1606.0</td>\n",
-              "      <td>0.1442</td>\n",
-              "      <td>0.2576</td>\n",
-              "      <td>0.3784</td>\n",
-              "      <td>0.1932</td>\n",
-              "      <td>0.3063</td>\n",
-              "      <td>0.08368</td>\n",
-              "      <td>0</td>\n",
+              "      <td>-0.167716</td>\n",
+              "      <td>-0.270710</td>\n",
+              "      <td>-0.154104</td>\n",
+              "      <td>-0.780055</td>\n",
+              "      <td>0.750137</td>\n",
+              "      <td>-0.257237</td>\n",
+              "      <td>0.034507</td>\n",
+              "      <td>0.005168</td>\n",
+              "      <td>4.99</td>\n",
+              "      <td>2</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>7</th>\n",
-              "      <td>1</td>\n",
-              "      <td>13.71</td>\n",
-              "      <td>20.83</td>\n",
-              "      <td>90.20</td>\n",
-              "      <td>577.9</td>\n",
-              "      <td>0.11890</td>\n",
-              "      <td>0.16450</td>\n",
-              "      <td>0.09366</td>\n",
-              "      <td>0.05985</td>\n",
-              "      <td>0.2196</td>\n",
+              "      <td>7.0</td>\n",
+              "      <td>-0.644269</td>\n",
+              "      <td>1.417964</td>\n",
+              "      <td>1.074380</td>\n",
+              "      <td>-0.492199</td>\n",
+              "      <td>0.948934</td>\n",
+              "      <td>0.428118</td>\n",
+              "      <td>1.120631</td>\n",
+              "      <td>-3.807864</td>\n",
+              "      <td>0.615375</td>\n",
               "      <td>...</td>\n",
-              "      <td>28.14</td>\n",
-              "      <td>110.60</td>\n",
-              "      <td>897.0</td>\n",
-              "      <td>0.1654</td>\n",
-              "      <td>0.3682</td>\n",
-              "      <td>0.2678</td>\n",
-              "      <td>0.1556</td>\n",
-              "      <td>0.3196</td>\n",
-              "      <td>0.11510</td>\n",
+              "      <td>1.943465</td>\n",
+              "      <td>-1.015455</td>\n",
+              "      <td>0.057504</td>\n",
+              "      <td>-0.649709</td>\n",
+              "      <td>-0.415267</td>\n",
+              "      <td>-0.051634</td>\n",
+              "      <td>-1.206921</td>\n",
+              "      <td>-1.085339</td>\n",
+              "      <td>40.80</td>\n",
               "      <td>-1</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>8</th>\n",
-              "      <td>1</td>\n",
-              "      <td>13.00</td>\n",
-              "      <td>21.82</td>\n",
-              "      <td>87.50</td>\n",
-              "      <td>519.8</td>\n",
-              "      <td>0.12730</td>\n",
-              "      <td>0.19320</td>\n",
-              "      <td>0.18590</td>\n",
-              "      <td>0.09353</td>\n",
-              "      <td>0.2350</td>\n",
+              "      <td>7.0</td>\n",
+              "      <td>-0.894286</td>\n",
+              "      <td>0.286157</td>\n",
+              "      <td>-0.113192</td>\n",
+              "      <td>-0.271526</td>\n",
+              "      <td>2.669599</td>\n",
+              "      <td>3.721818</td>\n",
+              "      <td>0.370145</td>\n",
+              "      <td>0.851084</td>\n",
+              "      <td>-0.392048</td>\n",
               "      <td>...</td>\n",
-              "      <td>30.73</td>\n",
-              "      <td>106.20</td>\n",
-              "      <td>739.3</td>\n",
-              "      <td>0.1703</td>\n",
-              "      <td>0.5401</td>\n",
-              "      <td>0.5390</td>\n",
-              "      <td>0.2060</td>\n",
-              "      <td>0.4378</td>\n",
-              "      <td>0.10720</td>\n",
-              "      <td>-1</td>\n",
+              "      <td>-0.073425</td>\n",
+              "      <td>-0.268092</td>\n",
+              "      <td>-0.204233</td>\n",
+              "      <td>1.011592</td>\n",
+              "      <td>0.373205</td>\n",
+              "      <td>-0.384157</td>\n",
+              "      <td>0.011747</td>\n",
+              "      <td>0.142404</td>\n",
+              "      <td>93.20</td>\n",
+              "      <td>3</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>9</th>\n",
-              "      <td>1</td>\n",
-              "      <td>12.46</td>\n",
-              "      <td>24.04</td>\n",
-              "      <td>83.97</td>\n",
-              "      <td>475.9</td>\n",
-              "      <td>0.11860</td>\n",
-              "      <td>0.23960</td>\n",
-              "      <td>0.22730</td>\n",
-              "      <td>0.08543</td>\n",
-              "      <td>0.2030</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>-0.338262</td>\n",
+              "      <td>1.119593</td>\n",
+              "      <td>1.044367</td>\n",
+              "      <td>-0.222187</td>\n",
+              "      <td>0.499361</td>\n",
+              "      <td>-0.246761</td>\n",
+              "      <td>0.651583</td>\n",
+              "      <td>0.069539</td>\n",
+              "      <td>-0.736727</td>\n",
               "      <td>...</td>\n",
-              "      <td>40.68</td>\n",
-              "      <td>97.65</td>\n",
-              "      <td>711.4</td>\n",
-              "      <td>0.1853</td>\n",
-              "      <td>1.0580</td>\n",
-              "      <td>1.1050</td>\n",
-              "      <td>0.2210</td>\n",
-              "      <td>0.4366</td>\n",
-              "      <td>0.20750</td>\n",
-              "      <td>-1</td>\n",
+              "      <td>-0.246914</td>\n",
+              "      <td>-0.633753</td>\n",
+              "      <td>-0.120794</td>\n",
+              "      <td>-0.385050</td>\n",
+              "      <td>-0.069733</td>\n",
+              "      <td>0.094199</td>\n",
+              "      <td>0.246219</td>\n",
+              "      <td>0.083076</td>\n",
+              "      <td>3.68</td>\n",
+              "      <td>1</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
-              "<p>10 rows × 32 columns</p>\n",
+              "<p>10 rows × 31 columns</p>\n",
               "</div>\n",
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cdf2a5af-29b0-43f6-baef-5196d49f08c3')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-11a404c1-8452-43f4-9c08-43e28f6335e6')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -999,12 +986,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-cdf2a5af-29b0-43f6-baef-5196d49f08c3 button.colab-df-convert');\n",
+              "        document.querySelector('#df-11a404c1-8452-43f4-9c08-43e28f6335e6 button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-cdf2a5af-29b0-43f6-baef-5196d49f08c3');\n",
+              "        const element = document.querySelector('#df-11a404c1-8452-43f4-9c08-43e28f6335e6');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -1024,8 +1011,8 @@
               "  </div>\n",
               "\n",
               "\n",
-              "    <div id=\"df-85a8e093-d7e0-4b2f-a47a-44c5cc397605\">\n",
-              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-85a8e093-d7e0-4b2f-a47a-44c5cc397605')\"\n",
+              "    <div id=\"df-4a209259-4d4b-48e5-bea1-c0f3822087f6\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4a209259-4d4b-48e5-bea1-c0f3822087f6')\"\n",
               "                title=\"Suggest charts\"\n",
               "                style=\"display:none;\">\n",
               "\n",
@@ -1144,7 +1131,7 @@
               "        }\n",
               "        (() => {\n",
               "          let quickchartButtonEl =\n",
-              "            document.querySelector('#df-85a8e093-d7e0-4b2f-a47a-44c5cc397605 button');\n",
+              "            document.querySelector('#df-4a209259-4d4b-48e5-bea1-c0f3822087f6 button');\n",
               "          quickchartButtonEl.style.display =\n",
               "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "        })();\n",
@@ -1155,67 +1142,43 @@
               "  </div>\n"
             ],
             "text/plain": [
-              "   diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
-              "0          1        17.99         10.38          122.80     1001.0   \n",
-              "1          1        20.57         17.77          132.90     1326.0   \n",
-              "2          1        19.69         21.25          130.00     1203.0   \n",
-              "3          1        11.42         20.38           77.58      386.1   \n",
-              "4          1        20.29         14.34          135.10     1297.0   \n",
-              "5          1        12.45         15.70           82.57      477.1   \n",
-              "6          1        18.25         19.98          119.60     1040.0   \n",
-              "7          1        13.71         20.83           90.20      577.9   \n",
-              "8          1        13.00         21.82           87.50      519.8   \n",
-              "9          1        12.46         24.04           83.97      475.9   \n",
-              "\n",
-              "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
-              "0          0.11840           0.27760         0.30010              0.14710   \n",
-              "1          0.08474           0.07864         0.08690              0.07017   \n",
-              "2          0.10960           0.15990         0.19740              0.12790   \n",
-              "3          0.14250           0.28390         0.24140              0.10520   \n",
-              "4          0.10030           0.13280         0.19800              0.10430   \n",
-              "5          0.12780           0.17000         0.15780              0.08089   \n",
-              "6          0.09463           0.10900         0.11270              0.07400   \n",
-              "7          0.11890           0.16450         0.09366              0.05985   \n",
-              "8          0.12730           0.19320         0.18590              0.09353   \n",
-              "9          0.11860           0.23960         0.22730              0.08543   \n",
-              "\n",
-              "   symmetry_mean  ...  texture_worst  perimeter_worst  area_worst  \\\n",
-              "0         0.2419  ...          17.33           184.60      2019.0   \n",
-              "1         0.1812  ...          23.41           158.80      1956.0   \n",
-              "2         0.2069  ...          25.53           152.50      1709.0   \n",
-              "3         0.2597  ...          26.50            98.87       567.7   \n",
-              "4         0.1809  ...          16.67           152.20      1575.0   \n",
-              "5         0.2087  ...          23.75           103.40       741.6   \n",
-              "6         0.1794  ...          27.66           153.20      1606.0   \n",
-              "7         0.2196  ...          28.14           110.60       897.0   \n",
-              "8         0.2350  ...          30.73           106.20       739.3   \n",
-              "9         0.2030  ...          40.68            97.65       711.4   \n",
-              "\n",
-              "   smoothness_worst  compactness_worst  concavity_worst  concave points_worst  \\\n",
-              "0            0.1622             0.6656           0.7119                0.2654   \n",
-              "1            0.1238             0.1866           0.2416                0.1860   \n",
-              "2            0.1444             0.4245           0.4504                0.2430   \n",
-              "3            0.2098             0.8663           0.6869                0.2575   \n",
-              "4            0.1374             0.2050           0.4000                0.1625   \n",
-              "5            0.1791             0.5249           0.5355                0.1741   \n",
-              "6            0.1442             0.2576           0.3784                0.1932   \n",
-              "7            0.1654             0.3682           0.2678                0.1556   \n",
-              "8            0.1703             0.5401           0.5390                0.2060   \n",
-              "9            0.1853             1.0580           1.1050                0.2210   \n",
-              "\n",
-              "   symmetry_worst  fractal_dimension_worst  Cluster  \n",
-              "0          0.4601                  0.11890       -1  \n",
-              "1          0.2750                  0.08902        0  \n",
-              "2          0.3613                  0.08758       -1  \n",
-              "3          0.6638                  0.17300       -1  \n",
-              "4          0.2364                  0.07678       -1  \n",
-              "5          0.3985                  0.12440       -1  \n",
-              "6          0.3063                  0.08368        0  \n",
-              "7          0.3196                  0.11510       -1  \n",
-              "8          0.4378                  0.10720       -1  \n",
-              "9          0.4366                  0.20750       -1  \n",
-              "\n",
-              "[10 rows x 32 columns]"
+              "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
+              "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
+              "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
+              "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
+              "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
+              "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
+              "5   2.0 -0.425966  0.960523  1.141109 -0.168252  0.420987 -0.029728  0.476201   \n",
+              "6   4.0  1.229658  0.141004  0.045371  1.202613  0.191881  0.272708 -0.005159   \n",
+              "7   7.0 -0.644269  1.417964  1.074380 -0.492199  0.948934  0.428118  1.120631   \n",
+              "8   7.0 -0.894286  0.286157 -0.113192 -0.271526  2.669599  3.721818  0.370145   \n",
+              "9   9.0 -0.338262  1.119593  1.044367 -0.222187  0.499361 -0.246761  0.651583   \n",
+              "\n",
+              "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
+              "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
+              "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
+              "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
+              "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
+              "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
+              "5  0.260314 -0.568671  ... -0.208254 -0.559825 -0.026398 -0.371427 -0.232794   \n",
+              "6  0.081213  0.464960  ... -0.167716 -0.270710 -0.154104 -0.780055  0.750137   \n",
+              "7 -3.807864  0.615375  ...  1.943465 -1.015455  0.057504 -0.649709 -0.415267   \n",
+              "8  0.851084 -0.392048  ... -0.073425 -0.268092 -0.204233  1.011592  0.373205   \n",
+              "9  0.069539 -0.736727  ... -0.246914 -0.633753 -0.120794 -0.385050 -0.069733   \n",
+              "\n",
+              "        V26       V27       V28  Amount  Cluster  \n",
+              "0 -0.189115  0.133558 -0.021053  149.62       -1  \n",
+              "1  0.125895 -0.008983  0.014724    2.69        0  \n",
+              "2 -0.139097 -0.055353 -0.059752  378.66       -1  \n",
+              "3 -0.221929  0.062723  0.061458  123.50       -1  \n",
+              "4  0.502292  0.219422  0.215153   69.99       -1  \n",
+              "5  0.105915  0.253844  0.081080    3.67        1  \n",
+              "6 -0.257237  0.034507  0.005168    4.99        2  \n",
+              "7 -0.051634 -1.206921 -1.085339   40.80       -1  \n",
+              "8 -0.384157  0.011747  0.142404   93.20        3  \n",
+              "9  0.094199  0.246219  0.083076    3.68        1  \n",
+              "\n",
+              "[10 rows x 31 columns]"
             ]
           },
           "metadata": {},
@@ -1226,30 +1189,37 @@
           "output_type": "stream",
           "text": [
             "\n",
-            "🧩 Silhouette Score: 0.0368 (higher = better clustering)\n",
-            "\n",
-            "📊 Cluster Counts:\n",
+            "📊 Cluster Distribution:\n",
             "Cluster\n",
-            " 1    269\n",
-            "-1    231\n",
-            " 0     64\n",
-            " 2      5\n",
-            "Name: count, dtype: int64\n"
+            "-1      98184\n",
+            " 9      19238\n",
+            " 565    11903\n",
+            " 1      11429\n",
+            " 562    10724\n",
+            "        ...  \n",
+            " 335        2\n",
+            " 534        2\n",
+            " 989        2\n",
+            " 168        2\n",
+            " 540        1\n",
+            "Name: count, Length: 1261, dtype: int64\n",
+            "\n",
+            "🧩 Silhouette Score (excluding noise): -0.0396\n"
           ]
         },
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 127752 (\\N{RAINBOW}) missing from font(s) DejaVu Sans.\n",
+            "/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 128179 (\\N{CREDIT CARD}) missing from font(s) DejaVu Sans.\n",
             "  fig.canvas.print_figure(bytes_io, **kw)\n"
           ]
         },
         {
           "data": {
-            "image/png": "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",
+            "image/png": "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",
             "text/plain": [
-              "<Figure size 800x600 with 1 Axes>"
+              "<Figure size 800x600 with 2 Axes>"
             ]
           },
           "metadata": {},
@@ -1258,13 +1228,12 @@
       ],
       "source": [
         "# =====================================\n",
-        "# 🧬 DBSCAN Clustering on Breast Cancer Dataset (Unsupervised Learning)\n",
+        "# 💳 DBSCAN Clustering on Credit Card Fraud Dataset\n",
         "# =====================================\n",
         "\n",
         "import pandas as pd\n",
         "import numpy as np\n",
         "import matplotlib.pyplot as plt\n",
-        "import seaborn as sns\n",
         "from sklearn.preprocessing import StandardScaler\n",
         "from sklearn.cluster import DBSCAN\n",
         "from sklearn.decomposition import PCA\n",
@@ -1279,7 +1248,7 @@
         "\n",
         "# Step 2️⃣: Load dataset\n",
         "df = pd.read_csv(io.BytesIO(uploaded[csv_filename]))\n",
-        "print(\"✅ File Loaded Successfully!\\n\")\n",
+        "print(\"✅ Credit Card Dataset Loaded Successfully!\\n\")\n",
         "display(df.head())\n",
         "\n",
         "# Step 3️⃣: Clean column names\n",
@@ -1287,54 +1256,73 @@
         "print(\"\\n📋 Columns in dataset:\")\n",
         "print(df.columns.tolist())\n",
         "\n",
-        "# Step 4️⃣: Data Preprocessing\n",
-        "# Drop 'id' or unnamed columns\n",
-        "df = df.drop(columns=[col for col in df.columns if 'id' in col.lower() or 'unnamed' in col.lower()], errors='ignore')\n",
-        "\n",
-        "# Convert 'diagnosis' to numeric for reference (not used for clustering)\n",
-        "if 'diagnosis' in df.columns:\n",
-        "    df['diagnosis'] = df['diagnosis'].map({'M': 1, 'B': 0})\n",
+        "# Step 4️⃣: Separate target (for reference only, NOT used in clustering)\n",
+        "if 'Class' in df.columns:\n",
+        "    y_true = df['Class']   # 1 = Fraud, 0 = Normal\n",
+        "    df = df.drop(columns=['Class'])\n",
         "\n",
-        "# Select only numeric columns\n",
+        "# Step 5️⃣: Select numerical features\n",
         "df_clean = df.select_dtypes(include=[np.number])\n",
         "\n",
-        "# Remove NaN or infinite values\n",
+        "# Handle missing or infinite values\n",
         "df_clean = df_clean.replace([np.inf, -np.inf], np.nan).dropna()\n",
         "\n",
-        "# Step 5️⃣: Standardize the data\n",
+        "# Step 6️⃣: Standardize data\n",
         "scaler = StandardScaler()\n",
         "X_scaled = scaler.fit_transform(df_clean)\n",
         "\n",
-        "# Step 6️⃣: Apply DBSCAN Clustering\n",
-        "dbscan = DBSCAN(eps=2.5, min_samples=5)  # you can tune eps\n",
+        "# Step 7️⃣: Apply DBSCAN\n",
+        "dbscan = DBSCAN(\n",
+        "    eps=1.5,        # Tunable parameter\n",
+        "    min_samples=5\n",
+        ")\n",
         "clusters = dbscan.fit_predict(X_scaled)\n",
         "\n",
-        "# Step 7️⃣: Add cluster labels to DataFrame\n",
+        "# Step 8️⃣: Add cluster labels\n",
         "df_clean['Cluster'] = clusters\n",
         "display(df_clean.head(10))\n",
         "\n",
-        "# Step 8️⃣: Evaluate clustering quality (only if >1 cluster)\n",
+        "# Step 9️⃣: Cluster distribution\n",
+        "print(\"\\n📊 Cluster Distribution:\")\n",
+        "print(df_clean['Cluster'].value_counts())\n",
+        "\n",
+        "# Step 🔟: Silhouette Score (excluding noise points)\n",
+        "mask = clusters != -1\n",
         "unique_clusters = len(set(clusters)) - (1 if -1 in clusters else 0)\n",
+        "\n",
         "if unique_clusters > 1:\n",
-        "    sil_score = silhouette_score(X_scaled, clusters)\n",
-        "    print(f\"\\n🧩 Silhouette Score: {sil_score:.4f} (higher = better clustering)\")\n",
+        "    sil_score = silhouette_score(X_scaled[mask], clusters[mask])\n",
+        "    print(f\"\\n🧩 Silhouette Score (excluding noise): {sil_score:.4f}\")\n",
         "else:\n",
-        "    print(\"\\n⚠️ Only one cluster found — silhouette score not applicable.\")\n",
-        "\n",
-        "# Step 9️⃣: Show cluster distribution\n",
-        "print(\"\\n📊 Cluster Counts:\")\n",
-        "print(df_clean['Cluster'].value_counts())\n",
+        "    print(\"\\n⚠️ Not enough clusters for silhouette evaluation.\")\n",
         "\n",
-        "# Step 🔟: Visualize Clusters (PCA 2D Projection)\n",
-        "pca = PCA(n_components=2)\n",
+        "# Step 1️⃣1️⃣: PCA Visualization (Sampling for performance)\n",
+        "pca = PCA(n_components=2, random_state=42)\n",
         "X_pca = pca.fit_transform(X_scaled)\n",
         "\n",
-        "plt.figure(figsize=(8,6))\n",
-        "sns.scatterplot(x=X_pca[:, 0], y=X_pca[:, 1], hue=clusters, palette='rainbow', legend='full')\n",
-        "plt.title(\"🌈 DBSCAN Clustering (PCA Visualization)\")\n",
+        "# Create DataFrame for plotting\n",
+        "plot_df = pd.DataFrame({\n",
+        "    'PC1': X_pca[:, 0],\n",
+        "    'PC2': X_pca[:, 1],\n",
+        "    'Cluster': clusters\n",
+        "})\n",
+        "\n",
+        "# Sample data to avoid rendering issues\n",
+        "plot_df_sample = plot_df.sample(n=10000, random_state=42)\n",
+        "\n",
+        "plt.figure(figsize=(8, 6))\n",
+        "plt.scatter(\n",
+        "    plot_df_sample['PC1'],\n",
+        "    plot_df_sample['PC2'],\n",
+        "    c=plot_df_sample['Cluster'],\n",
+        "    cmap='tab10',\n",
+        "    alpha=0.6\n",
+        ")\n",
+        "\n",
+        "plt.title(\"💳 DBSCAN Clustering on Credit Card Transactions (PCA Sample)\")\n",
         "plt.xlabel(\"Principal Component 1\")\n",
         "plt.ylabel(\"Principal Component 2\")\n",
-        "plt.legend(title=\"Cluster\")\n",
+        "plt.colorbar(label=\"Cluster\")\n",
         "plt.show()\n"
       ]
     }